TY  - JOUR
T1  - Syllable-based Korean POS Tagging Based on Combining a Pre-analyzed Dictionary with Machine Learning
AU  - Lee, Chung-Hee 
AU  - Lim, Joon-Ho 
AU  - Lim, Soojong 
AU  - Kim, Hyun-Ki 
JO  - Journal of KIISE, JOK
PY  - 2016
DA  - 2016/1/14
DO  - 
KW  - morphological analysis
KW  - POS tagging
KW  - machine learning
KW  - pre-analyzed dictionary
AB  - This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.